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    陆枫, 王子锐, 廖小飞, 金海. 基于细粒度标签的在线视频广告投放机制研究[J]. 计算机研究与发展, 2014, 51(12): 2733-2745. DOI: 10.7544/issn1000-1239.2014.20131337
    引用本文: 陆枫, 王子锐, 廖小飞, 金海. 基于细粒度标签的在线视频广告投放机制研究[J]. 计算机研究与发展, 2014, 51(12): 2733-2745. DOI: 10.7544/issn1000-1239.2014.20131337
    Lu Feng, Wang Zirui, Liao Xiaofei, Jin Hai. Online Video Advertising Based on Fine-Grained Video Tags[J]. Journal of Computer Research and Development, 2014, 51(12): 2733-2745. DOI: 10.7544/issn1000-1239.2014.20131337
    Citation: Lu Feng, Wang Zirui, Liao Xiaofei, Jin Hai. Online Video Advertising Based on Fine-Grained Video Tags[J]. Journal of Computer Research and Development, 2014, 51(12): 2733-2745. DOI: 10.7544/issn1000-1239.2014.20131337

    基于细粒度标签的在线视频广告投放机制研究

    Online Video Advertising Based on Fine-Grained Video Tags

    • 摘要: 随着互联网的发展,对精彩视频点进行标注、评论和分享成为趋势.这类群体智慧信息的有效利用将有助于提升视频广告的投放效果.首先将用户提供的细粒度视频标签收集起来,通过视频时间轴加权计算生成视频热点,进而利用视频热点描述信息基于分类匹配的思想来选取广告,最后找出视频热点内用户对视频关注度下降幅度最大的时间点投放广告.实验证明,在数量为百万级的视频集合中,该方法选取的广告与视频的相关性达到85%左右.用户在广告播放过程中关闭广告的概率小于10%.与目前广泛应用的广告投放方式相比,广告的平均播放时间能提升21.5%,广告点击率能从0.65%提高至0.73%.

       

      Abstract: With the development of the Internet, it has been a trend of manual tagging, labeling and sharing videos. Rational use of these swarm intelligence will help to improve the effectiveness of video advertising. The method presented in this paper first collects the fine-grained user video tags, and generates the video hotspots by the video timeline-weighted method. Then, based on the idea of the classification matching, the description of the video hotspots can be used to select the advertising. At last, the time points that the popular attention has dropped by the biggest level are found to put advertising. Experiments show that, among the mega-scale video set, the content correlation between the hotspot and the advertisements selected by this method can reach 85%. The probability that the users close ads windows is less than 10%. Compared with the ads system that has been widely adopted so far, the average broadcast time of the new method can be increased by 21.5%, the click-through rate is improved from 0.65% to 0.73%.

       

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